Advanced digital twin-enabled fault diagnosis framework for unmanned vehicle systems

The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.